Smoking related environmental microbes affecting the pulmonary microbiome in Chinese population

Airway microbiome: environmental exposure-respiratory health nexus

Toxicants such as smoke, biofuel, and pollutants constantly challenge our respiratory health, but little is known about the pathophysiological processes involved. In a new report, Lin et al. provide evidence that our bacterial and fungal lung populations orchestrate the interplay between environmental exposure and lung functions, thereby conditioning health outcomes.

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM_2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

Association between lipid-A-producing oral bacteria of different potency and fractional exhaled nitric oxide in a Norwegian population-based adult cohort

BACKGROUND

Lipid A is the primary immunostimulatory part of the lipopolysaccharide (LPS) molecule. The inflammatory response of LPS varies and depends upon the number of acyl chains and phosphate groups in lipid A which is specific for a bacterial species or strain. Traditional LPS quantification assays cannot distinguish between the acylation degree of lipid A molecules, and therefore little is known about how bacteria with different inflammation-inducing potencies affect fractional exhaled nitric oxide (F_eNO). We aimed to explore the association between pro-inflammatory hexa- and less inflammatory penta-acylated LPS-producing oral bacteria and F_eNO as a marker of airway inflammation.

METHODS

We used data from a population-based adult cohort from Norway (n = 477), a study center of the RHINESSA multi-center generation study. We applied statistical methods on the bacterial community- (prediction with MiRKAT) and genus-level (differential abundance analysis with ANCOM-BC) to investigate the association between the oral microbiota composition and F_eNO.

RESULTS

We found the overall composition to be significantly associated with increasing F_eNO levels independent of covariate adjustment, and abundances of 27 bacterial genera to differ in individuals with high F_eNO vs. low F_eNO levels. Hexa- and penta-acylated LPS producers made up 2.4% and 40.8% of the oral bacterial genera, respectively. The Bray-Curtis dissimilarity within hexa- and penta-acylated LPS-producing oral bacteria was associated with increasing F_eNO levels independent of covariate adjustment. A few single penta-acylated LPS producers were more abundant in individuals with low F_eNO vs. high F_eNO, while hexa-acylated LPS producers were found not to be enriched.

CONCLUSIONS

In a population-based adult cohort, F_eNO was observed to be associated with the overall oral bacterial community composition. The effect of hexa- and penta-acylated LPS-producing oral bacteria was overall significant when focusing on Bray-Curtis dissimilarity within each of the two communities and F_eNO levels, but only penta-acylated LPS producers appeared to be reduced or absent in individuals with high F_eNO. It is likely that the pro-inflammatory effect of hexa-acylated LPS producers is counteracted by the dominance of the more abundant penta-acylated LPS producers in this population-based adult cohort involving mainly healthy individuals.

Modeling and analyzing single-cell multimodal data with deep parametric inference

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Human matters in asthma: Considering the microbiome in pulmonary health

Microbial communities form an important symbiotic ecosystem within humans and have direct effects on health and well-being. Numerous exogenous factors including airborne triggers, diet, and drugs impact these established, but fragile communities across the human lifespan. Crosstalk between the mucosal microbiota and the immune system as well as the gut-lung axis have direct correlations to immune bias that may promote chronic diseases like asthma. Asthma initiation and pathogenesis are multifaceted and complex with input from genetic, epigenetic, and environmental components. In this review, we summarize and discuss the role of the airway microbiome in asthma, and how the environment, diet and therapeutics impact this low biomass community of microorganisms. We also focus this review on the pediatric and Black populations as high-risk groups requiring special attention, emphasizing that the whole patient must be considered during treatment. Although new culture-independent techniques have been developed and are more accessible to researchers, the exact contribution the airway microbiome makes in asthma pathogenesis is not well understood. Understanding how the airway microbiome, as a living entity in the respiratory tract, participates in lung immunity during the development and progression of asthma may lead to critical new treatments for asthma, including population-targeted interventions, or even more effective administration of currently available therapeutics.